Probabilistic Feature Augmentation for AIS-Based Multi-Path Long-Term Vessel Trajectory Forecasting
arxiv(2023)
摘要
Maritime transportation is paramount in achieving global economic growth,
entailing concurrent ecological obligations in sustainability and safeguarding
endangered marine species, most notably preserving large whale populations. In
this regard, the Automatic Identification System (AIS) data plays a significant
role by offering real-time streaming data on vessel movement, allowing enhanced
traffic monitoring. This study explores using AIS data to prevent
vessel-to-whale collisions by forecasting long-term vessel trajectories from
engineered AIS data sequences. For such a task, we have developed an
encoder-decoder model architecture using Bidirectional Long Short-Term Memory
Networks (Bi-LSTM) to predict the next 12 hours of vessel trajectories using 1
to 3 hours of AIS data as input. We feed the model with probabilistic features
engineered from historical AIS data that refer to each trajectory's potential
route and destination. The model then predicts the vessel's trajectory,
considering these additional features by leveraging convolutional layers for
spatial feature learning and a position-aware attention mechanism that
increases the importance of recent timesteps of a sequence during temporal
feature learning. The probabilistic features have an F1 Score of approximately
85
effectiveness in augmenting information to the neural network. We test our
model on the Gulf of St. Lawrence, a region known to be the habitat of North
Atlantic Right Whales (NARW). Our model achieved a high R2 score of over 98
using various techniques and features. It stands out among other approaches as
it can make complex decisions during turnings and path selection. Our study
highlights the potential of data engineering and trajectory forecasting models
for marine life species preservation.
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